INTELLIGENT STOCK TRADING SYSTEM WITH PRICE TREND PREDICTION AND REVERSAL RECOGNITION USING DUAL-MODULE NEURAL NETWORKS

This article presents an intelligent stock trading system that can gen erate timely stock trading suggestions according to the prediction of short-term trends of price movement using dual-module neural networks (dual net). Retrospective technical indicators extracted from raw pric e and volume time series data gathered from the market are used as ind ependent variables for neural modeling. Both neural network modules of the dual net learn the correlation between the trends of price moveme nt and the retrospective technical indicators by use of a modified bac k-propagation learning algorithm. Reinforcing the temporary correlatio n between the neural weights and the training patterns, dual modules o f neural networks are respectively trained on a short-term and a long- term moving-window of training patterns. An adaptive reversal recognit ion mechanism that can self-tune thresholds for identification of the timing for buying or selling stocks has also been developed in our sys tem. It is shown that the proposed dual net architecture generalizes b etter than one single-module neural network. According to the features of acceptable rate of returns and consistent quality of trading sugge stions shown in the performance evaluation, an intelligent stock tradi ng system with price trend prediction and reversal recognition can be realized using the proposed dual-module neural networks.